Introduction to Genetic Algorithm.

1. Vardhaman College of
Engineering.
Department of Mechanical
Engineering.
Course: Engineering Design.
Presented by:
M. Harsha
17881D9503.
Design of Spur Gear using Genetic Algorithm.
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CONTENTS
Introduction to Genetic Algorithm.
Mutation.
Cross over, Selection.
Working Principle.
Termination Criteria.
Spur Gear Parameters.
Application of GA to Spur Gear.
Advantages of GA.
Conclusion.
References.
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3. Metaheuristic inspired by the process of natural
selection.
Generate high-quality solutions to optimization.
Is an iterative process called as generation.
Value of the objective function in the optimization
problem will be solved.
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4. Cells are basics of all living things.
So in each cell there must have set of
chromosomes. These are strings of DNA.
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13. Spur Gear design using GA:
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Objective Function is :
Minimise the centre distance of gears.
Minimise the Weight.
Minimise the tooth deflection.
Decision variable as :
Module, face width and number of teeth onpinion.(M,B,T).
Constraints : Bending stress, Contact stress.

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 Centre Distance:
Smaller Gear sets.
Space occupancy.
 Gear Weight:
Less in weight
Material, which leads to cost reduction and easy
assembly.
Gear Tooth Deflection:
Minimum.
Failure

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Constraints:
Functional Relationship.
Design variables & Design Parameters.
Bending Stress: Limited to maximum allowable BS.
Contact Stress: Smaller than the allowable CS.
Formulation:
Design Variables: x= ( m, b, t)
Objective Function : F(x) =f(x1) +f (x2) +f(x3)
Constraints : g1(x) < BS. Design
g2(x) < CS Design

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Using Conventional Calculations & Genetic Algorithms.
For Data,
Power P = 8 KW.
Transmission ratio i = 3.2.
Speed of pinion Np = 720 rpm.

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Advantages:
Quality Solutions in Complex engineering problems.
Solutions are better with time.
Easy to implement.
Same encode- changes the fitness value.
Multi objective optimization.
Disadvantages:
 Computational time
 Slower than some optimization processes.

18. Applications:
Engineering design.
Traffic and Shipment Routing. (Travelling Salesman
Problem).
Additive manufacturing.
Robotics Etc….
Inside this big group, there are many notable subsets,
such as:
1.Genetic programming (GP).
2.Evolution Strategies (ES).
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CONCLUSIONS:
Since GA is random function search and optimization
technique, the chance of getting global optimum is more.
Using GA we can get optimality and best feasible solution
within the given conditions in objective function.
The results of proposed algorithm have been compared to
those of the traditional techniques, such as, graphical
technique for best feasible solution.

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